{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "izNeUi--hSSA"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/integrations/openai/semantic_search_openai.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/pinecone-io/examples/blob/master/integrations/openai/semantic_search_openai.ipynb)\n",
        "\n",
        "# Semantic Search with Pinecone and OpenAI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LI76-BMFhUmS"
      },
      "source": [
        "In this guide you will learn how to use the OpenAI Embedding API to generate language embeddings, and then index those embeddings in the Pinecone vector database for fast and scalable vector search.\n",
        "\n",
        "This is a powerful and common combination for building semantic search, question-answering, threat-detection, and other applications that rely on NLP and search over a large corpus of text data.\n",
        "\n",
        "The basic workflow looks like this:\n",
        "\n",
        "**Embed and index**\n",
        "\n",
        "* Use the OpenAI Embedding API to generate vector embeddings of your documents (or any text data).\n",
        "* Upload those vector embeddings into Pinecone, which can store and index millions/billions of these vector embeddings, and search through them at ultra-low latencies.\n",
        "\n",
        "**Search**\n",
        "\n",
        "* Pass your query text or document through the OpenAI Embedding API again.\n",
        "* Take the resulting vector embedding and send it as a query to Pinecone.\n",
        "* Get back semantically similar documents, even if they don't share any keywords with the query.\n",
        "\n",
        "![Architecture overview](https://files.readme.io/6a3ea5a-pinecone-openai-overview.png)\n",
        "\n",
        "Let's get started..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nXzzYE0hhU64"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FNk3hfyAtRNj"
      },
      "source": [
        "We first need to setup our environment and retrieve API keys for OpenAI and Pinecone. Let's start with our environment, we need HuggingFace *Datasets* for our data, and the OpenAI and Pinecone clients:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m201.4/201.4 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m225.1/225.1 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.9/75.9 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.9/76.9 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires tiktoken, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -qU \\\n",
        "    pinecone-client==3.0.2 \\\n",
        "    openai==1.10.0 \\\n",
        "    datasets==2.16.1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Creating Embeddings\n",
        "\n",
        "Then we initialize our connection to OpenAI Embeddings *and* Pinecone vector DB. Sign up for an API key over at [OpenAI](https://platform.openai.com) and [Pinecone](https://app.pinecone.io)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from openai import OpenAI\n",
        "\n",
        "client = OpenAI(\n",
        "    api_key=\"OPENAI_API_KEY\"\n",
        ")  # get API key from platform.openai.com"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pw8cbO3HtRNo"
      },
      "source": [
        "We can now create embeddings with the OpenAI v3 small embedding model like so:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "CreateEmbeddingResponse(data=[Embedding(embedding=[-0.0007019874756224453, 0.017813093960285187, 0.028484342619776726, -0.01655358262360096, -0.04467806592583656, -0.03371616080403328, 0.02429058402776718, -0.015460160560905933, 0.0147542804479599, -0.006034583784639835, 0.03413138538599014, -0.010325227864086628, 0.004678186494857073, -0.006508630700409412, 0.06505170464515686, 0.07252573221921921, -0.004359848331660032, 0.013335599564015865, -0.026740403845906258, 0.03684417903423309, 0.004705868195742369, 0.04675418511033058, -0.021384017542004585, 0.03440820053219795, 0.012664321810007095, -0.021896127611398697, -0.040553510189056396, -0.024913419038057327, 0.05112787336111069, -0.06244963780045509, -0.01853281632065773, -0.04124554991722107, 0.01914181001484394, -0.03044973500072956, -0.040165968239307404, 0.054698795080184937, 0.06560533493757248, 0.0040449704974889755, -0.049799155443906784, -0.04819362610578537, -0.009563985280692577, -0.012491311877965927, 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-0.027351709082722664, 0.010137027129530907, 0.010593649931252003, -0.002323068678379059, -0.010662143118679523, -0.03847808390855789, -0.008751938119530678, 0.011309025809168816, -0.025220802053809166, -0.008356197737157345, 0.007907185703516006, -0.022648492828011513, -0.021994000300765038, -0.021324286237359047, -0.04295298829674721, 0.002374438801780343, -0.0041666836477816105, -0.01171998679637909, -0.008097445592284203, -0.009132456965744495, -0.024048803374171257, 0.035494815558195114, -0.028584590181708336, 0.0362558551132679, -0.0040449174121022224, 0.0023249713703989983, -0.029650043696165085, -0.000983641715720296, -0.003281976794824004, 0.04383579269051552, 0.01621011085808277, 0.03972618654370308, 0.041796211153268814, 0.00012414433876983821, 0.005962733179330826, -0.009817391633987427, 0.04371402785181999, -0.04520566388964653, 0.005064708646386862, 0.019208600744605064, 0.02173524722456932, -0.00017765483062248677, -0.009277054108679295, -0.011210091412067413, 0.0068150958977639675, -0.0043683587573468685, 0.04645376652479172, 0.028280174359679222, 0.059665385633707047, -0.03217669203877449, 0.01604268327355385, 0.018630212172865868, 0.005840967409312725, 0.0065335119143128395, -0.017960498109459877, 0.010989390313625336, -0.0025570879224687815, 0.014368399046361446, -0.003234411822631955, -0.01564694382250309, 0.02226797491312027, 0.04121782258152962, 0.007355432957410812, 0.007869133725762367, -0.016834162175655365, -0.006651472765952349, 0.003945982549339533, -0.02301379106938839, -0.027032073587179184, -0.009345547296106815, -0.05537313222885132, -0.00762940663844347, -0.009216170758008957, 0.06313572078943253, 0.007754978258162737, 0.01901073195040226, 0.021567819640040398, -0.027747448533773422, -0.011514506302773952, -0.021765688434243202, -0.0023192635271698236, -0.023135557770729065, 0.005810525733977556, 0.040517669171094894, 0.020806781947612762, 0.008706275373697281, 0.0286454726010561, 0.009779339656233788, 0.022998571395874023, -0.009596690535545349, 0.017716966569423676, -0.04800628125667572, 0.0032058728393167257, -0.014383619651198387, 0.003976423759013414, -0.045753609389066696, -0.005555578041821718, 0.01837145909667015, -0.02290724590420723, 0.0042465925216674805, 0.019208600744605064, -0.0046233064495027065, 0.017549538984894753, 0.003301002783700824, -0.010578429326415062, 0.023729167878627777, -0.02945217303931713, 0.017047252506017685, 0.043988000601530075, -0.014436892233788967, -0.024733737111091614, 0.007085264660418034, -0.002511425642296672, 0.01578393019735813, -0.0029870744328945875, -0.014528216794133186, -0.0011786577524617314, 0.011187260039150715, 0.026529787108302116, -0.009436871856451035, 0.0011272876290604472, 0.01639275997877121, -0.007614186033606529, -0.00643457705155015, -0.02566220425069332, 0.012298375368118286, 0.017123356461524963, 0.01261801179498434, -0.001627670251764357, -0.003949787467718124, -0.00968040432780981, -0.03981751203536987, 0.020197950303554535, -0.01837145909667015, -0.0007795884157530963, 0.014581489376723766, 0.04188753664493561, 0.006232901941984892, -0.017001591622829437, 0.019178159534931183, -0.006887394469231367], index=1, object='embedding')], model='text-embedding-3-small', object='list', usage=Usage(prompt_tokens=13, total_tokens=13))"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "MODEL = \"text-embedding-3-small\"\n",
        "\n",
        "res = client.embeddings.create(\n",
        "    input=[\n",
        "        \"Sample document text goes here\",\n",
        "        \"there will be several phrases in each batch\"\n",
        "    ], model=MODEL\n",
        ")\n",
        "res"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Egz35DjtRNo",
        "outputId": "78c4c022-d3a8-4fa7-8fa8-59c45eca1298"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "vector 0: 1536\n",
            "vector 1: 1536\n"
          ]
        }
      ],
      "source": [
        "print(f\"vector 0: {len(res.data[0].embedding)}\\nvector 1: {len(res.data[1].embedding)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZguN6obBtRNp",
        "outputId": "bd20d4be-0772-4383-9d1e-9960d8b5f183"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# we can extract embeddings to a list\n",
        "embeds = [record.embedding for record in res.data]\n",
        "len(embeds)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U4WJwy58tRNp"
      },
      "source": [
        "Next, we initialize our index to store vector embeddings with Pinecone."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OnyoBu-BRNOs",
        "outputId": "4d40b553-4ff2-4336-baab-24ff6424fead"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1536"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(embeds[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Initialize connection to Pinecone, you can get a free API key in the [Pinecone dashboard](https://app.pinecone.io)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from pinecone import Pinecone\n",
        "\n",
        "pc = Pinecone(api_key=\"...\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'dimension': 1536,\n",
              " 'index_fullness': 0.0,\n",
              " 'namespaces': {},\n",
              " 'total_vector_count': 0}"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import time\n",
        "from pinecone import ServerlessSpec\n",
        "\n",
        "spec = ServerlessSpec(cloud=\"aws\", region=\"us-west-2\")\n",
        "\n",
        "index_name = 'semantic-search-openai'\n",
        "\n",
        "# check if index already exists (it shouldn't if this is your first run)\n",
        "if index_name not in pc.list_indexes().names():\n",
        "    # if does not exist, create index\n",
        "    pc.create_index(\n",
        "        index_name,\n",
        "        dimension=len(embeds[0]),  # dimensionality of text-embed-3-small\n",
        "        metric='dotproduct',\n",
        "        spec=spec\n",
        "    )\n",
        "    # wait for index to be initialized\n",
        "    while not pc.describe_index(index_name).status['ready']:\n",
        "        time.sleep(1)\n",
        "\n",
        "# connect to index\n",
        "index = pc.Index(index_name)\n",
        "time.sleep(1)\n",
        "# view index stats\n",
        "index.describe_index_stats()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLCLmeZRtRNq"
      },
      "source": [
        "## Populating the Index\n",
        "\n",
        "Now we will take 1K questions from the TREC dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JiSfCTK-lhXx",
        "outputId": "bcb82771-a0d5-4c63-d2ad-69a963f93620"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:datasets.builder:Found cached dataset trec (/root/.cache/huggingface/datasets/trec/default/2.0.0/f2469cab1b5fceec7249fda55360dfdbd92a7a5b545e91ea0f78ad108ffac1c2)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'coarse_label', 'fine_label'],\n",
              "    num_rows: 1000\n",
              "})"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# load the first 1K rows of the TREC dataset\n",
        "trec = load_dataset('trec', split='train[:1000]')\n",
        "trec"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2m7a_zl8lt2e",
        "outputId": "7e2bc141-27bf-4580-c632-642a65c652d8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'text': 'How did serfdom develop in and then leave Russia ?',\n",
              " 'coarse_label': 2,\n",
              " 'fine_label': 26}"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trec[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sJwnszP0ljN6"
      },
      "source": [
        "Then we create a vector embedding for each phrase using OpenAI, and `upsert` the ID, vector embedding, and original text for each phrase to Pinecone."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9bc55e10545a4fbaa63359cb58dd54c2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/32 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "count = 0  # we'll use the count to create unique IDs\n",
        "batch_size = 32  # process everything in batches of 32\n",
        "for i in tqdm(range(0, len(trec['text']), batch_size)):\n",
        "    # set end position of batch\n",
        "    i_end = min(i+batch_size, len(trec['text']))\n",
        "    # get batch of lines and IDs\n",
        "    lines_batch = trec['text'][i: i+batch_size]\n",
        "    ids_batch = [str(n) for n in range(i, i_end)]\n",
        "    # create embeddings\n",
        "    res = client.embeddings.create(input=lines_batch, model=MODEL)\n",
        "    embeds = [record.embedding for record in res.data]\n",
        "    # prep metadata and upsert batch\n",
        "    meta = [{'text': line} for line in lines_batch]\n",
        "    to_upsert = zip(ids_batch, embeds, meta)\n",
        "    # upsert to Pinecone\n",
        "    index.upsert(vectors=list(to_upsert))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iW_iM_M_tRNr"
      },
      "source": [
        "---\n",
        "\n",
        "# Querying\n",
        "\n",
        "With our data indexed, we're now ready to move onto performing searches. This follows a similar process to indexing. We start with a text `query`, that we would like to use to find similar sentences. As before we encode this with OpenAI's text similarity Babbage model to create a *query vector* `xq`. We then use `xq` to query the Pinecone index."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "query = \"What caused the 1929 Great Depression?\"\n",
        "\n",
        "xq = client.embeddings.create(input=query, model=MODEL).data[0].embedding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RxCKv-UktRNs"
      },
      "source": [
        "Now query..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'matches': [{'id': '932',\n",
              "              'metadata': {'text': 'Why did the world enter a global '\n",
              "                                   'depression in 1929 ?'},\n",
              "              'score': 0.751888752,\n",
              "              'values': []},\n",
              "             {'id': '787',\n",
              "              'metadata': {'text': \"When was `` the Great Depression '' ?\"},\n",
              "              'score': 0.597448647,\n",
              "              'values': []},\n",
              "             {'id': '400',\n",
              "              'metadata': {'text': 'What crop failure caused the Irish Famine '\n",
              "                                   '?'},\n",
              "              'score': 0.367482603,\n",
              "              'values': []},\n",
              "             {'id': '835',\n",
              "              'metadata': {'text': 'What were popular songs and types of songs '\n",
              "                                   'in the 1920s ?'},\n",
              "              'score': 0.324545294,\n",
              "              'values': []},\n",
              "             {'id': '262',\n",
              "              'metadata': {'text': 'When did World War I start ?'},\n",
              "              'score': 0.320995867,\n",
              "              'values': []}],\n",
              " 'namespace': '',\n",
              " 'usage': {'read_units': 6}}"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "res = index.query(vector=[xq], top_k=5, include_metadata=True)\n",
        "res"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I2iydlXVjmMZ"
      },
      "source": [
        "The response from Pinecone includes our original text in the `metadata` field, let's print out the `top_k` most similar questions and their respective similarity scores."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.75: Why did the world enter a global depression in 1929 ?\n",
            "0.60: When was `` the Great Depression '' ?\n",
            "0.37: What crop failure caused the Irish Famine ?\n",
            "0.32: What were popular songs and types of songs in the 1920s ?\n",
            "0.32: When did World War I start ?\n"
          ]
        }
      ],
      "source": [
        "for match in res['matches']:\n",
        "    print(f\"{match['score']:.2f}: {match['metadata']['text']}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wu7LmvmIjqPq"
      },
      "source": [
        "Looks good, let's make it harder and replace *\"depression\"* with the incorrect term *\"recession\"*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.63: Why did the world enter a global depression in 1929 ?\n",
            "0.55: When was `` the Great Depression '' ?\n",
            "0.34: What were popular songs and types of songs in the 1920s ?\n",
            "0.33: What crop failure caused the Irish Famine ?\n",
            "0.29: What is considered the costliest disaster the insurance industry has ever faced ?\n"
          ]
        }
      ],
      "source": [
        "query = \"What was the cause of the major recession in the early 20th century?\"\n",
        "\n",
        "# create the query embedding\n",
        "xq = client.embeddings.create(input=query, model=MODEL).data[0].embedding\n",
        "\n",
        "# query, returning the top 5 most similar results\n",
        "res = index.query(vector=[xq], top_k=5, include_metadata=True)\n",
        "\n",
        "for match in res['matches']:\n",
        "    print(f\"{match['score']:.2f}: {match['metadata']['text']}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FXFhFri0jzkG"
      },
      "source": [
        "And again..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.62: Why did the world enter a global depression in 1929 ?\n",
            "0.54: When was `` the Great Depression '' ?\n",
            "0.34: What were popular songs and types of songs in the 1920s ?\n",
            "0.33: What crop failure caused the Irish Famine ?\n",
            "0.32: What do economists do ?\n"
          ]
        }
      ],
      "source": [
        "query = \"Why was there a long-term economic downturn in the early 20th century?\"\n",
        "\n",
        "# create the query embedding\n",
        "xq = client.embeddings.create(input=query, model=MODEL).data[0].embedding\n",
        "\n",
        "# query, returning the top 5 most similar results\n",
        "res = index.query(vector=[xq], top_k=5, include_metadata=True)\n",
        "\n",
        "for match in res['matches']:\n",
        "    print(f\"{match['score']:.2f}: {match['metadata']['text']}\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Looks great, our semantic search pipeline is clearly able to identify the meaning between each of our queries and return the most semantically similar questions from the already indexed questions.\n",
        "\n",
        "Once we're finished with the index we delete it to save resources."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "pc.delete_index(index_name)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "ItsvY1lej6dz"
      },
      "source": [
        "---"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
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